Abstract
Retinopathy of Prematurity is a disease that affects premature infants having low birth weight. The disease may lead to blindness unless timely treatment is not provided. Because of the high birth rate premature babies and expanded neonatal care, the incidence of ROP is worrying in India today. There is an urgent need to create awareness about disease. The researchers propose a new approach of grading ROP with feed forward networks using second order texture features. Experiments are conducted with six different architectures of Feed Forward Networks. Second order texture features mean, entropy, contrast, correlation, homogeneity, energy from Gray level co-occurrence matrix (GLCM) are considered. The results obtained indicate Feed forward network offers an easy yet effective paradigm for ROP Grading.
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Giraddi, S., Chickerur, S., Annigeri, N. (2021). Grading Retinopathy of Prematurity with Feedforward Network. In: Abraham, A., Jabbar, M., Tiwari, S., Jesus, I. (eds) Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019). SoCPaR 2019. Advances in Intelligent Systems and Computing, vol 1182. Springer, Cham. https://doi.org/10.1007/978-3-030-49345-5_18
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DOI: https://doi.org/10.1007/978-3-030-49345-5_18
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